Power structured, accurate, and auditable data extraction from large and complex databases using Xevyte’s AI-powered Extraction APIs.
Power structured, accurate, and auditable data extraction from large and complex databases using Xevyte’s AI-powered Extraction APIs.
Organizations sit on a growing mountain of data: transactional systems, legacy databases, semi‑structured stores, logs, and third‑party feeds. Extracting accurate, timely, and contextually relevant information from these sources is essential for analytics, automation, compliance, and product innovation, yet it remains costly, brittle, and slow when done manually or with brittle rule engines.
Xevyte solves this problem with a suite of AI-driven Extraction APIs that automate the discovery, normalization, and delivery of high‑quality data from large and heterogeneous databases. Our approach combines foundation models fine‑tuned for structured data understanding, schema inference engines, and end‑to‑end observability so teams can trust extracted outputs in production.
Xevyte is designed for data teams, product engineers, and automation owners who need reliable extraction at scale without rearchitecting their data stacks.
Xevyte’s AI Data Extraction APIs automate discovery, normalization, and structured retrieval from complex enterprise datasets with high accuracy and auditability.
Prebuilt connectors for common databases and storage systems (Postgres, MySQL, SQL Server, Oracle, MongoDB, Elasticsearch, S3, Redshift, BigQuery) plus a lightweight SDK for building custom connectors. These connectors are optimized for speed, fault tolerance, and scalability, enabling seamless integration into diverse environments. They automatically handle schema drift, incremental updates, and version control for evolving datasets. With built-in data security and encryption, organizations can safely connect to critical systems without compromising compliance. Intelligent caching mechanisms reduce extraction latency, improving overall pipeline performance. The result is a unified, plug-and-play connectivity layer that simplifies data integration across hybrid and multi-cloud architectures.
A set of APIs and orchestration primitives to schedule extractions, define pipelines (extract → validate → enrich → deliver), retry policies, backpressure handling, and integration points for existing schedulers. Xevyte’s orchestration engine allows users to visually design and monitor extraction workflows through intuitive dashboards. Advanced dependency management ensures that complex pipelines execute in sequence or parallel as needed, with real-time error recovery and alerting. The system supports version-controlled workflow templates for repeatability across environments. Integration with popular CI/CD tools ensures automated deployment of extraction pipelines, minimizing downtime. This orchestration framework helps enterprises achieve operational resilience, faster delivery, and continuous optimization of data flows.
Professional services for model fine‑tuning, connector development, on‑prem or VPC deployment, and SLA‑backed support packages. Our enterprise team collaborates closely with clients to customize AI extraction models for specific domain vocabularies and regulatory standards. We provide dedicated implementation experts to assist in migration, integration, and scaling activities. Continuous training and enablement sessions ensure that client teams maximize ROI from the platform. Managed service options are available for organizations seeking end-to-end pipeline monitoring and maintenance. With proactive support and 24/7 service availability, Xevyte ensures mission-critical data extraction workflows operate without disruption.
Xevyte delivers highly accurate, explainable, and schema-aware data extraction using advanced AI models trained for structured and semi-structured data. Its enterprise-grade connectors, orchestration engine, and scalable architecture ensure reliable, high-performance extraction across diverse systems.
Unlike simple pattern‑matching or rule engines, Xevyte uses model ensembles and context‑aware reasoning tailored to structured data. Every extraction includes explainability metadata (why a field was mapped, confidence bands, counterexamples) to make outputs auditable and actionable.
Xevyte treats the schema as first‑class: the system infers relationships, respects constraints, and preserves provenance when resolving ambiguous mappings. This reduces manual cleanup and accelerates time‑to‑insight.
Built from the ground up for enterprise workloads: streaming extraction, CDC support, horizontal scalability, and predictable performance. Our connectors and pipeline controls make it straightforward to incorporate Xevyte into existing data platforms.
A consistent RESTful/GraphQL interface, SDKs in major languages (Python, Java, Node.js), Postman collections, and clear examples for common use cases (billing data extraction, product catalog ingestion, customer 360 pipelines). Rapid onboarding with sandbox environments and prebuilt templates.
Extraction is secure by default. Credentials are never stored in plaintext, data transfers can be restricted to private networks, and every transformation is logged to provide the auditability required by regulated industries.
Xevyte focuses on measurable outcomes: reduced manual ETL effort, faster analytics delivery, improved data quality, and faster product integrations. We align extraction outputs to business KPIs so teams can measure impact quickly.
Our vision is to make innovation purposeful, responsible, and human-centered. We are building an ecosystem where AI, cybersecurity, and digital engineering converge to create smarter, safer, and more sustainable enterprises. Through constant evolution, collaboration, and foresight, we aim to set new standards for intelligent transformation. Because for us, innovation isn’t about keeping up with the future it’s about creating it.